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中国农学通报

中国农学通报 ›› 2026, Vol. 42 ›› Issue (15): 171-180.doi: 10.11924/j.issn.1000-6850.casb2025-0690

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

贵州农业气象灾害损失时空分布特征

曾晓珊1,2(), 左晋1(), 陈芳1, 张波1, 管玥3   

  1. 1 贵州省生态与农业气象中心， 贵阳 550002
    2 中国气象局兰州干旱气象研究所， 兰州 730020
    3 贵州省黔西南布依苗族自治州气象局， 贵州兴义 562400
  • 收稿日期:2025-08-18 修回日期:2026-03-10 出版日期:2026-08-15 发布日期:2026-08-13
  • 通讯作者:
    左晋，女，1985年出生，高级工程师，硕士，研究方向：应用气象。通信地址：550002 贵州省贵阳市南明区新华路翠微巷9号，E-mail：。
  • 作者简介:

    曾晓珊，女，1993年出生，工程师，硕士，研究方向：应用气象。通信地址：550002 贵州省贵阳市南明区新华路翠微巷9号，E-mail：。

  • 基金资助:
    中国气象局兰州干旱气象研究所干旱气象科学研究基金“基于WOFOST模型的云贵地区不同播期春玉米的干旱损失评估研究”(IAM202320); 贵州省科技厅项目“基于风险区划的春茶霜冻害气象指数保险费率厘定研究”(黔科合基础-ZK[2023]一般230); 贵州省科技厅项目“贵州铁皮石斛气候适宜性区划研究”(黔科合基础-ZK[2022]一般272)

Spatial and Temporal Distribution Characteristics of Agrometeorological Disaster Loss in Guizhou

ZENG Xiaoshan1,2(), ZUO Jin1(), CHEN Fang1, ZHANG Bo1, GUAN Yue3   

  1. 1 Guizhou Ecological Meteorology and Agrometeorology Center, Guiyang 550002
    2 Lanzhou Institute of Arid Meteorology, CMA, Lanzhou 730020
    3 Qianxinan Bouyei and Miao Autonomous Prefecture Meteorology Bureau of Guizhou Province, Xingyi, Guizhou 562400
  • Received:2025-08-18 Revised:2026-03-10 Published:2026-08-15 Online:2026-08-13

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摘要：

基于2009—2020年的农业灾情数据，综合构建了灾损指数(DLI)，运用线性回归、M-K趋势检验及ArcGIS空间热点分析等方法，揭示了近年来贵州省农业气象灾害损失的时空分布特征，为农业防灾减灾提供科学依据。结果表明：（1）2009—2020年贵州省农业气象灾害损失总体呈“西部、北部和东部重，中部、南部轻”的空间分布格局，其中，毕节市为四类主要农业灾害（干旱、洪涝、风雹、低温冷冻和雪灾）重叠重灾区；（2）2009—2020年贵州省年均灾损指数由高到低依次为：干旱(0.23)>洪涝(0.11)>风雹(0.05)>低温冷冻和雪灾(0.03)，干旱是影响贵州最严重的农业气象灾害，其损失空间分布呈现西北高东南低的特点，尽管99%的县（市、区）的干旱灾害损失呈下降趋势（5成以上达到显著水平），显示出积极变化，但其潜在威胁依然显著；（3）贵州省洪涝、低温冷冻和雪灾的灾害损失高值区集中分布在西部、北部和东南部边缘地区，其中，低温冷冻和雪灾受灾面积呈下降趋势的区域占比(94.3%)>洪涝(64.8%)，洪涝灾害损失变化趋势的空间分异明显，中部、西北部和南部边缘地区呈增加趋势，并向东部扩展；同时，二者绝收面积呈增加趋势的区域占比均大于受灾面积，表明此类灾害极端破坏性增强；（4）贵州西部既是风雹灾害损失的热点区域，又是灾损增速的高值区。总体而言，贵州农业气象灾害的高风险区域主要集中在西部、北部和东部，灾害损失指数虽整体呈下降趋势，但灾害的极端破坏性增强，呈现出“气候极端性—高产值—高风险”的灾害风险新格局。

关键词: 贵州, 灾损指数, 农业气象灾害损失, 时空特征

Abstract:

Based on disaster data from 2009 to 2020, this study comprehensively constructed a disaster loss index (DLI) and employed methods including linear regression and Mann-Kendall trend test, combined with spatial hotspot analysis in ArcGIS, to reveal the spatiotemporal characteristics of agrometeorological disaster losses in Guizhou in recent years, thereby providing scientific basis for disaster prevention and mitigation. The results showed that: (1) from 2009 to 2020, agricultural meteorological disaster losses in Guizhou generally exhibited a spatial distribution pattern with higher severity in the western, northern, and eastern regions compared to the central and southern areas, among which Bijie City emerged as a multi-hazard overlap zone affected by all four disaster types; (2) from 2009 to 2020, the annual average DLI in Guizhou exhibited the following order: drought (0.23) > flood (0.11) > wind and hail (0.05) > low-temperature freezing and snow (0.03). Drought was the most severe agricultural meteorological hazard affecting Guizhou, displaying a northwest-high and southeast-low spatial pattern. Notably, 99% of counties showed declining trends in drought losses, with over 50% reaching statistically significant levels; (3) in Guizhou, the high-value zones of disaster losses from flood, low-temperature freezing and snow were distributed in the western, northern, and southeastern marginal regions. 94.3% of areas showed a decreasing trend in the area affected by low-temperature freezing and snow, which was higher than the proportion for flood (64.8%). Flood exhibited significant spatial heterogeneity, with an increasing trend observed in the central, northwestern, and southern marginal regions, while the affected area also expanding eastward. The proportion of areas with an increasing trend for complete crop failure area due to both flood and low-temperature freezing and snow was higher than that for the affected area, indicating an increase in the extremality of the disasters; (4) western Guizhou served as both a hotspot for wind-hail disaster losses and a high-value zone of loss increase rates. Overall, the high-risk areas for agrometeorological disaster in Guizhou were located in the western, northern and eastern regions. Although the DLI had generally showed a downward trend, the extreme destructiveness of the disasters had increased.

Key words: Guizhou, disaster loss index (DLI), agrometeorological disaster loss, spatial and temporal distribution characteristics

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